Researchers at Chan Zuckerberg Biohub San Francisco and Stanford University now offer a novel solution to this dilemma: an AI-driven Virtual Lab through which a team of AI agents, each equipped with varied scientific expertise, can tackle sophisticated and open-ended scientific problems by formulating, refining, and carrying out a complex research strategy — these agents can even conduct virtual experiments, producing results that can be validated in real-life laboratories.
In a study published in Nature on July 29, 2025, co–senior authors John Pak of CZ Biohub SF and Stanford’s James Zou describe their Virtual Lab platform, in which a human user creates a “Principal Investigator” AI agent (the PI) that assembles and directs a team of additional AI agents emulating the specialized research roles seen in science labs. The human researcher proposes a scientific question, and then monitors meetings in which the PI agent exchanges ideas with the team of specialist agents to advance the research. The agents are run by a large language model (LLM), giving them scientific reasoning and decision-making capabilities.
The authors then used the Virtual Lab platform to investigate a timely research question: designing antibodies or Nanobodies to bind to the spike protein of new variants of the SARS-CoV-2 virus. After just a few days working together, the Virtual Lab team had designed and implemented an innovative computational pipeline, and had presented Pak and Zou with blueprints for dozens of binders, two of which showed particular promise against new SARS-CoV-2 strains when subsequently tested in Pak’s lab. The overall Virtual Lab study was led by Kyle Swanson, a Ph.D. student in Zou’s group.
“What was once this crazy science fiction idea is now a reality,” said Pak, group leader of the Biohub SF Protein Sciences Platform. “The AI agents came up with a pipeline that was quite creative. But at the same time, it wasn’t outrageous or nonsensical. It was very reasonable – and they were very fast.”
Zou is a pioneering AI researcher who has been recognized widely for breakthroughs in using AI for biomedical research, including winning the International Society of Computational Biology’s 2025 Overton Prize and being named in the New York Times’ 2024 Good Tech Awards for SyntheMol, an AI system that can design and validate novel antibiotics.
“This is the first demonstration of autonomous AI agents really solving a challenging research problem, from start to finish,” said Zou, an associate professor of biomedical data science who leads Stanford University’s AI for Health program and is also a CZ Biohub SF Investigator. “The AI agents made good decisions about complex problems and were able to quickly design dozens of protein candidates that we could then test in lab experiments.”
A fortuitous real-world meeting
It’s become increasingly common for human scientists to employ LLMs to help with science research, such as analyzing data, writing code, and even designing proteins. Zou and Pak’s Virtual Lab platform, however, is to their knowledge the first to apply multistep reasoning and interdisciplinary expertise to successfully address an open-ended research question.
Zou and Pak first met at one of the biweekly Biohub SF Investigator Program meetings. “I had just seen James give a talk at the previous Investigator meeting, where he said he wished he could do more experimental work,” Pak said. “So I decided to introduce myself.”
That conversation, in the spring of 2024, sparked a collaboration that drew on Zou’s AI expertise and Pak’s expertise in protein science.
In addition to the PI agent and specialist agents, their Virtual Lab platform includes a Scientific Critic agent, a generalist whose role is to ask probing questions and inject a dose of skepticism into the process. “We found the Critic to be quite essential, and also reduced hallucinations,” Zou said.
While human researchers participated in AI scientists’ meetings and offered guidance at key moments, their words made up only about 1% of all conversations. The vast majority of discussions, decisions, and analyses were performed by the AI agents themselves.
In this study, the Virtual Lab team came up with 92 new “Nanobodies” (tiny proteins that work like antibodies), and experiments in Pak’s lab found that two bound to the so-called spike protein of recent SARS-CoV-2 variants, a significant enough finding that Pak expects to publish studies on them.
“You’d think there’d be no way AI agents talking together could propose something akin to what a human scientist would come up with, but we found here that they really can,” said Pak. “It’s pretty shocking.”
When asked if he’s worried about AI scientists replacing him, Pak says no. Instead, he thinks these new virtual collaborators will just enhance his work.
“This project opened the door for our Protein Science team to test a lot more well-conceived ideas very quickly,” he said. “The Virtual Lab gave us more work, in a sense, because it gave us more ideas to test. If AI can produce more testable hypotheses, that’s more work for everyone.”
The results, said Pak and Zou, not only demonstrate the potential benefits of human–AI collaborations but also highlight the importance of diverse perspectives in science. Even in these virtual settings, instructing agents to assume different roles and bring varying perspectives to the table resulted in better outcomes than one AI agent working alone, they said. And because the discussions result in a transcript that human team members can access and review, researchers can feel confident about why certain decisions were made and probe further if they have questions or concerns.
“The agents and the humans are all speaking the same language, so there’s nothing ‘black box’ about it, and the collaboration can progress very smoothly,” Pak said. “It was a really positive experience overall, and I feel pretty confident about applying the Virtual Lab in future research.”
Zou says the existing platform is designed for biomedical research questions, but modifications would allow it to be used in a much wider array of scientific disciplines.
“We’re demonstrating a new paradigm where AI is not just a tool we use for a specific step in our research, but it can actually be a primary driver of the whole process to generate discoveries,” said Zou. “It’s a big shift, and we’re excited to see how it helps us advance in all areas of research.”
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About CZ Biohub San Francisco: A nonprofit biomedical research center founded in 2016, CZ Biohub SF is part of the CZ Biohub Network, a group of research institutes created and supported by the Chan Zuckerberg Initiative. CZ Biohub SF’s researchers, engineers, and data scientists, in collaboration with colleagues at our partner universities — Stanford University; the University of California, Berkeley; and the University of California, San Francisco — seek to understand the fundamental mechanisms underlying disease and develop new technologies that will lead to actionable diagnostics and effective therapies. Learn more at czbiohub.org/sf.
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